In a typical real-time analytics system, an application system provides customer transaction data to an analytical engine that is capable of executing analytical tasks. An example of such an analytical engine is a prediction engine that provides useful, predictive output relating to a transaction with a customer. An analytical engine is capable of processing real-time data from a customer to execute analytical tasks and to generate output in real time. Often, the analytical engine will use the real-time data in coordination with a data mining model to generate a predictive output. A data mining model typically contains rules and patterns derived from historical data that has been collected, synthesized, and formatted.
In many instances, a predictive output generated upon execution of an analytical task is fed into a business rule engine. The business rule engine will use the predictive output in conjunction with its rule set to determine if certain events should be triggered in the application system. For example, the business rule engine may determine that a special promotional offer should be provided to a particular customer given the content of the predictive output and the nature of the transaction with that customer. In some instances, the application system may directly process the predictive output.
Typically, the application system will need predictive output in a short period of time. For example, if a call-center agent is using a customer interaction center application system to interact with a customer online, the agent will typically need predictive output quickly in order to effectively interact with the customer. However, a prediction engine used in a typical analytics system may be required to wait until the application system has provided all of the input parameters that are required for task execution before the engine is able to execute a given prediction task during a customer interactive session. This can result in performance degradation.